metadata

license: cc-by-nc-4.0
language:
  - hu
metrics:
  - accuracy
  - f1
model-index:
  - name: Hun_RoBERTa_large_Plain
    results:
      - task:
          type: text-classification
        metrics:
          - type: accuracy
            value: 0.79
          - type: f1
            value: 0.79
widget:
  - text: >-
      A tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete
      szerinti szállításához szükséges adminisztratív okmányban...
    example_title: Incomprehensible
  - text: >-
      Az AEO-engedély birtokosainak listáján – keresésre – megjelenő
      információk: az engedélyes neve, az engedélyt kibocsátó ország...
    example_title: Comprehensible

Model description

Cased fine-tuned XLM-RoBERTa-large model for Hungarian, trained to classify sentences based on their Plain Language properties.

Intended uses & limitations

The model is designed to classify sentences as either "comprehensible" or "not comprehensible" (according to Plain Language guidelines):

Label_0 - "comprehensible" - The sentence is in Plain Language.
Label_1 - "not comprehensible" - The sentence is not in Plain Language.

Training

Fine-tuned version of the original xlm-roberta-large model, trained on a dataset of Hungarian legal and administrative texts.

Eval results

Class	Precision	Recall	F-Score
Comprehensible / Label_0	0.76	0.86	0.81
Not comprehensible / Label_1	0.83	0.72	0.77
accuracy			0.79
macro avg	0.80	0.79	0.79
weighted avg	0.79	0.79	0.79

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")

Citation:

@PhDThesis{ Uveges:2024, author = {{"U}veges, Istv{'a}n}, title = {K{"o}z{'e}rthet{"o} és automatiz{'a}ci{'o} - k{'i}s{'e}rletek a jog, term{'e}szetesnyelv-feldolgoz{'a}s {'e}s informatika hat{'a}r{'a}n.}, year = {2024}, school = {Szegedi Tudom{'a}nyegyetem} }